Buffett Revisited

The Oracle of Omaha is an investment legend whose tireless work ethic and down-to-earth philosophy has helped him grow Berkshire Hathaway into one of the largest companies in the world. In a recent article for Nasdaq, Validea CEO John Reese digs into Buffett’s stock selection method and offers five stocks picks that make the grade under his Buffett-inspired screening model (the first of which are currently in the Berkshire portfolio):

  • Apple Inc. (AAPL),the tech industry behemoth, gets a green light based on its predictable and growing earnings and moderate debt level. The company’s return-on-retained earnings (change in 10-year earnings-per-share divided by earnings retained during that period) well exceeds the minimum requirement of 15%.
  • Verisk Analytics, Inc. (VRSK) is a data analytics provider servicing customers in insurance, natural resources, healthcare, financial services, government and risk management. Stable profitability and earnings-per-share growth (10 yr. average of 18.1%) make the grade in the Buffett-based model, which likes the company’s higher than average return-on-assets (10 yr. average of 10.4%).
  • Hasbro Inc. (HAS) is a global branded-play company. Under the Buffett-inspired model, HAS gets high marks for its predictable earnings and strong debt coverage (all debt can be paid off in five years). Return-on-equity of 21.7% (10 yr. average) indicates durable competitive advantage and exceeds the model’s minimum requirement of 15%.
  • Syntel Inc. (SYNT) is a global provider of information technology with a consistent earnings history, conservative debt profile and long-term EPS growth of 15.7%, a plus under the Buffett-inspired screen. The company also gets a thumb’s up due to an expected compound rate of return in excess of 18%.
  • Sherwin Williams Co. (SHW), a household name in paints and related products, gets a nod from the Buffett-based screening model due to its durable competitive advantage, modest leverage and return-on-total capital (25.5%) which is more than double the model’s minimum requirement.

 

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